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sparsness_analysis.py
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import __main__
import numpy
import pylab
import sys
from topo.misc.filepath import normalize_path, application_path
from contrib.modelfit import *
import contrib.dd
import noiseEstimation
import contrib.JanA.dataimport
import visualization
from contrib.JanA.ofestimation import *
def SparsnessAnalysis():
pylab.rcParams['xtick.major.pad']='7'
import scipy
import scipy.stats
import numpy.random
from scipy import linalg
from matplotlib.ticker import MaxNLocator, NullLocator
contrib.modelfit.save_fig_directory='/home/antolikjan/Doc/reports/Sparsness/PieceWise/'
#res = contrib.dd.loadResults("results3.dat")
res = contrib.dd.loadResults("newest_dataset.dat")
#dataset_node = res.children[0].children[3]
dataset_node = res.children[0].children[0]
#LOAD NON-NORMALIZED DATA
training_set = dataset_node.data["training_set"]
validation_set = dataset_node.data["validation_set"]
training_inputs= dataset_node.data["training_inputs"]
validation_inputs= dataset_node.data["validation_inputs"]
raw_validation_set = dataset_node.data["raw_validation_set"]
num_trials = numpy.shape(raw_validation_set)[0]
node = dataset_node.children[0].children[0]
print dataset_node.children[0].children_params
#node = dataset_node.children[10]
if __main__.__dict__.get('Alg','GLM') == 'GLM':
sizex = numpy.sqrt(numpy.shape(validation_inputs)[1])
sizey = numpy.sqrt(numpy.shape(validation_inputs)[1])
K = node.data["Kernels"]
print 'Treshold:',K[:,sizex*sizey]
rfs = numpy.reshape(node.data["Kernels"][:,0:sizex*sizey],(numpy.shape(training_set)[1],sizex,sizey))
training_set = training_set[1:,:]
validation_set = validation_set[1:,:]
training_inputs= training_inputs[1:,:]
validation_inputs= validation_inputs[1:,:]
#for i in xrange(0,len(raw_validation_set)):
# raw_validation_set[i] = raw_validation_set[i][1:,:]
else:
rfs = node.data["ReversCorrelationRFs"]
raw_validation_data_set=numpy.rollaxis(numpy.array(raw_validation_set),2)
pylab.figure()
contrib.JanA.visualization.showRFS(rfs)
pylab.colorbar()
pred_act = numpy.array(node.data["ReversCorrelationPredictedActivities"])
val_pred_act = numpy.array(node.data["ReversCorrelationPredictedValidationActivities"])
if __main__.__dict__.get('Lateral',False):
glm = node.data["GLM"]
val_pred_act_raw = []
for i in xrange(0,num_trials):
val_pred_act_raw.append(glm.sample_from_recurrent_model(validation_inputs,K))
val_pred_act_raw = numpy.array(val_pred_act_raw)
#val_pred_act=numpy.mean(val_pred_act_raw,axis=0)
#val_pred_act_nl = numpy.hstack([ glm.response(validation_inputs,numpy.delete(validation_set*0,[i],axis=1),numpy.array([K[i]])) for i in xrange(0,103)])
#val_pred_act = numpy.hstack([ glm.response(validation_inputs,numpy.delete(val_pred_act_nl,[i],axis=1),numpy.array([K[i]])) for i in xrange(0,103)])
else:
val_pred_act_nl = val_pred_act
# fit the predicted activities to sigmoid again
# this time note that we are fitting it to measured activities that were this time not zero meaned and unit variance scaled
ofs = run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(pred_act),num_bins=10,display=True)
release_fig('EstimatedOF.png')
pred_act = apply_output_function(numpy.mat(pred_act),ofs)
val_pred_act = apply_output_function(numpy.mat(val_pred_act),ofs)
#ofs = fit_sigmoids_to_of(numpy.mat(training_set),numpy.mat(pred_act),offset=False)
#release_fig('EstimatedOF.png')
#pred_act = apply_sigmoid_output_function(numpy.mat(pred_act),ofs,offset=False)
#val_pred_act = apply_sigmoid_output_function(numpy.mat(val_pred_act),ofs,offset=False)
# determine signal and noise power and created predictions with gaussian noise of corresponding variance
signal_power,noise_power,normalized_noise_power,training_prediction_power,validation_prediction_power,signal_power_variance = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), pred_act, val_pred_act)
nt,nn = numpy.shape(pred_act)
numpy.random.randn(nt,nn)
print numpy.argmax(validation_prediction_power)
print validation_prediction_power[numpy.argmin(validation_prediction_power)]
pylab.rc('font', size=9)
pylab.figure(dpi=400,facecolor='w',figsize=(10,5))
ax = pylab.subplot(111)
ax.yaxis.set_major_locator(MaxNLocator(3))
pylab.imshow(training_set[:600,:].T,cmap='gray',interpolation='nearest',vmin=0,vmax=10)
pylab.xlabel('Image #')
pylab.ylabel('Neuron #')
pylab.colorbar(shrink=0.27,aspect=10,pad=0.02,ticks=[0,10,20])
pylab.gca().yaxis.set_major_locator(MaxNLocator(4))
pylab.gca().xaxis.set_ticks_position('top')
pylab.gca().xaxis.set_label_position('top')
release_fig('PopulationResponses.svg')
pylab.rc('font', size=15)
val_pred_act = val_pred_act * (val_pred_act > 0.0)
pred_act = pred_act * (pred_act > 0.0)
print 'Mean prediction power before neuron selection:',numpy.mean(validation_prediction_power)
(ranks,correct,pred) = performIdentification(validation_set,val_pred_act)
print "Correct:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(val_pred_act - validation_set,2))
# Exclude neurons with weak RFs from further analysis
rfs_mag=numpy.sum(numpy.power(numpy.reshape(numpy.abs(numpy.array(rfs)),(len(rfs),numpy.size(rfs[0]))),2),axis=1)
visualization.showRFS(rfs)
release_fig('RFsAll.png')
pylab.figure()
pylab.hist(rfs_mag)
pylab.xlabel('RFs magnitued')
pylab.figure()
pylab.plot(normalized_noise_power ,validation_prediction_power,'ro')
pylab.xlabel('normalized_noise_power')
pylab.ylabel('validation_prediction_power')
to_delete1 = numpy.array(numpy.nonzero((rfs_mag < 7000000) * 1.0))[0]
to_delete2 = numpy.array(numpy.nonzero((numpy.array(normalized_noise_power) > 85) * 1.0))[0]
to_delete = numpy.unique(numpy.append(to_delete1,to_delete2))
print to_delete
to_delete = [2, 3 , 4 , 5 , 6 , 7 , 9 , 10 , 11 , 12 , 13 , 14 , 18 , 22 , 26 , 28 , 29 , 31, 32 , 34 , 35 , 36, 37 , 38 , 41 , 44 , 49 , 51 , 54 , 55 , 57 , 59 , 60 , 63 , 67 , 68 , 70 , 72 , 73 , 74 , 76 , 77 , 79 , 81 , 82 , 84, 86, 87 , 88 , 90 , 94 , 95 , 97 , 98 , 99 ,100, 102]
print 'Number of neurons excluded:', len(to_delete)
training_set = numpy.delete(training_set, to_delete, axis = 1)
validation_set = numpy.delete(validation_set, to_delete, axis = 1)
pred_act = numpy.delete(pred_act, to_delete, axis = 1)
val_pred_act = numpy.delete(val_pred_act, to_delete, axis = 1)
val_pred_act_nl = numpy.delete(val_pred_act_nl, to_delete, axis = 1)
validation_prediction_power = numpy.delete(validation_prediction_power , to_delete)
normalized_noise_power = numpy.delete(normalized_noise_power , to_delete)
rfs = numpy.delete(rfs, to_delete, axis = 0)
for i in xrange(0,num_trials):
raw_validation_set[i] = numpy.delete(raw_validation_set[i], to_delete, axis = 1)
print 'Mean prediction power after neuron selection:',numpy.mean(validation_prediction_power)
(ranks,correct,pred) = performIdentification(validation_set,val_pred_act)
print "Correct:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(val_pred_act - validation_set,2))
visualization.showRFS(rfs)
release_fig('RFs.png')
(num_trials,num_pres,num_neurons) = numpy.shape(numpy.array(raw_validation_set))
raw_validation_set_flattened = numpy.reshape(raw_validation_set,(num_trials*num_pres,num_neurons))
pylab.figure(dpi=100,facecolor='w',figsize=(6,5))
pylab.hist(validation_prediction_power)
pylab.xlabel('Fraction of explained signal power ')
pylab.ylabel('# neurons')
# SHOW MINIMUM FIRING RATES, HISTOGRAMS OF MEAN FIRING RATES AND HISTOGRAM OF FIRING RATES
#print "Minimum activity levels for raw validation activities:", numpy.min(raw_validation_data_set,axis=0)
#print "Minimum activity levels for triggered activities:", numpy.min(training_set,axis=0)
#print "Minimum activity levels for predicted activities:", numpy.min(pred_act,axis=0)
#print "Maximum activity levels for triggered activities:", numpy.max(training_set,axis=0)
#print "Maximum activity levels for predicted activities:", numpy.max(pred_act,axis=0)
pylab.figure(dpi=100,facecolor='w',figsize=(15,11))
pylab.subplot(3,2,1)
pylab.hist(numpy.mean(training_set,axis=0))
pylab.xlabel('Mean triggered firing rate')
pylab.subplot(3,2,2)
pylab.hist(numpy.mean(pred_act,axis=0))
pylab.xlabel('Mean predicted firing rate')
pylab.subplot(3,2,3)
nonz = training_set.flatten()
nonz =nonz[numpy.nonzero(training_set.flatten())]
bins,edge = numpy.histogram(nonz,50)
pylab.plot(bins,'x',label='data')
ex = numpy.random.standard_exponential((1, len(training_set.flatten()))).flatten() * numpy.mean(nonz)
bins,edge = numpy.histogram(ex,50)
pylab.plot(bins,label='Exponential distribution')
pylab.xscale('log')
pylab.yscale('log')
pylab.xlabel('Triggered non-zero firing rates (pooled across population)')
pylab.legend()
pylab.subplot(3,2,4)
nonz = pred_act.flatten()
nonz =nonz[numpy.nonzero(pred_act.flatten())]
bins,edge = numpy.histogram(nonz,50)
pylab.plot(bins,'x')
ex = numpy.random.standard_exponential((1, len(pred_act.flatten()))).flatten() * numpy.mean(nonz)
bins,edge = numpy.histogram(ex,50)
pylab.plot(bins,label='Exponential distribution')
pylab.xlabel('Predicted non-zero firing rate (pooled across population)')
pylab.xscale('log')
pylab.yscale('log')
pylab.subplot(3,2,5)
nonz = training_set.flatten()
nonz =nonz[numpy.nonzero(training_set.flatten())]
bins,edge = numpy.histogram(nonz,50)
pylab.plot(bins,'x')
pylab.xlabel('Triggered non-zero firing rates (pooled across population)')
pylab.subplot(3,2,6)
nonz = pred_act.flatten()
nonz =nonz[numpy.nonzero(pred_act.flatten())]
bins,edge = numpy.histogram(nonz,50)
pylab.plot(bins,'x')
pylab.xlabel('Predicted non-zero firing rate (pooled across population)')
release_fig('Firing_rate_distributions.pdf')
ex = numpy.random.exponential(numpy.mean(raw_validation_data_set),(1, 10*len(training_set.flatten()))).flatten()
norm = numpy.random.normal(loc=numpy.exp(numpy.mean(raw_validation_data_set)),scale=0.5,size=(1, 10*len(training_set.flatten()))).flatten()
nonz=ex.flatten()
bins1,edge1 = numpy.histogram(nonz,50)
bins1 = bins1*1.0/numpy.sum(bins1)
bins3,edge3 = numpy.histogram(numpy.log(nonz),50)
bins3 = bins3*1.0/numpy.sum(bins3)
nonz=numpy.exp(norm.flatten())
bins2,edge2 = numpy.histogram(nonz,50)
bins2 = bins2*1.0/numpy.sum(bins2)
bins4,edge4 = numpy.histogram(numpy.log(nonz),50)
bins4 = bins4*1.0/numpy.sum(bins4)
pylab.figure()
pylab.subplot(221)
pylab.plot(bins3,label='logged exp')
pylab.plot(bins4,label='logged lognormal')
pylab.legend()
pylab.subplot(222)
pylab.plot(bins1,label='exp')
pylab.plot(bins2,label='lognormal')
pylab.legend()
pylab.subplot(223,yscale='log')
pylab.plot(bins3,label='logged exp')
pylab.plot(bins4,label='logged lognormal')
pylab.legend()
pylab.subplot(224,xscale='log',yscale='log')
pylab.plot(bins1,label='exp')
pylab.plot(bins2,label='lognormal')
pylab.legend()
pylab.figure()
pylab.hist(raw_validation_data_set.flatten()[numpy.nonzero(raw_validation_data_set.flatten())],bins=1000)
pylab.figure()
pylab.hist(validation_set.flatten()[numpy.nonzero(validation_set.flatten())],bins=1000)
#perform sampling of the model prediction from poisson distribution
if __main__.__dict__.get('Lateral',False):
pred_act_singletrial = val_pred_act_raw
pred_act_avg = val_pred_act
else:
pred_act_avg = numpy.zeros(numpy.shape(val_pred_act))
pred_act_singletrial = numpy.zeros(numpy.shape(raw_validation_set))
for x in xrange(0,numpy.shape(val_pred_act)[0]):
for y in xrange(0,numpy.shape(val_pred_act)[1]):
pred_act_singletrial[:,x,y] = numpy.random.poisson(lam=val_pred_act[x,y],size=(num_trials,1)).flatten()
pred_act_avg[x,y] = numpy.mean(pred_act_singletrial[:,x,y])
pylab.figure(dpi=100,facecolor='w',figsize=(17,12))
(bins,edges) = numpy.histogram(raw_validation_data_set.flatten()[numpy.nonzero(raw_validation_data_set.flatten())],bins=numpy.exp(numpy.arange(-4,10,0.4)))
bins = bins*1.0/numpy.sum(bins)/(edges[1:]-edges[:-1])
(bins_normal,edges_normal) = numpy.histogram(raw_validation_data_set.flatten()[numpy.nonzero(raw_validation_data_set.flatten())],bins=400)
bins_normal = bins_normal*1.0/numpy.sum(bins_normal)/(edges_normal[1:]-edges_normal[:-1])
print min(raw_validation_data_set.flatten())
logged = numpy.log(raw_validation_data_set.flatten()[numpy.nonzero(raw_validation_data_set.flatten())])
(bins_logged,edges_logged) = numpy.histogram(logged,bins=20)
bins_logged = bins_logged*1.0/numpy.sum(bins_logged)/(edges_logged[1]-edges_logged[0])
pylab.subplot(331)
pylab.title('Raw validation set')
xs = edges_normal[:-1]+(edges_normal[1]-edges_normal[0])/2
pylab.bar(xs,bins_normal,width=(edges_normal[1]-edges_normal[0])/2,color='b')
pylab.plot(xs,numpy.exp(-numpy.power(numpy.log(xs)-numpy.mean(logged),2)/(2*numpy.var(logged)))/(xs*numpy.sqrt(2*numpy.pi*numpy.var(logged))),'g',lw=2)
pylab.plot(xs,numpy.exp(-xs/numpy.mean(raw_validation_data_set))/numpy.mean(raw_validation_data_set),'r',lw=2)
pylab.axis(xmin=0,xmax=4)
pylab.ylabel('Probability density of\n firing rates',multialignment='center')
pylab.subplot(334)
xs = edges_logged[:-1]+(edges_logged[1]-edges_logged[0])/2
pylab.plot(xs,bins_logged,'o')
xs = numpy.arange(edges_logged[0],edges_logged[-1],(edges_logged[-1]-edges_logged[0])/200)
pylab.plot(xs,numpy.exp(-numpy.power(xs-numpy.mean(logged),2)/(2*numpy.var(logged)))/numpy.sqrt(2*numpy.pi*numpy.var(logged)),'g',lw=2)
pylab.ylabel('Probability density of\n log firing rates',multialignment='center')
pylab.subplot(337, xscale='log',yscale='log')
xs = edges[:-1]+(edges[1:]-edges[:-1])/2
pylab.plot(xs,bins,'o')
xs = numpy.exp(numpy.arange(-4,10,0.05))
pylab.plot(xs,numpy.exp(-numpy.power(numpy.log(xs)-numpy.mean(logged),2)/(2*numpy.var(logged)))/(xs*numpy.sqrt(2*numpy.pi*numpy.var(logged))),'g',lw=2)
pylab.plot(xs,numpy.exp(-xs/numpy.mean(raw_validation_data_set))/numpy.mean(raw_validation_data_set),'r',lw=2)
pylab.axis(xmin=10e-3,xmax=20,ymin=10e-4,ymax=10)
pylab.ylabel('Probability density of\n firing rates',multialignment='center')
pylab.xlabel('Firing rate')
(bins,edges) = numpy.histogram(validation_set.flatten()[numpy.nonzero(validation_set.flatten())],bins=numpy.exp(numpy.arange(-4,10,0.4)))
bins = bins*1.0/numpy.sum(bins)/(edges[1:]-edges[:-1])
logged = numpy.log(validation_set.flatten()[numpy.nonzero(validation_set.flatten())])
(bins_logged,edges_logged) = numpy.histogram(logged,bins=20)
bins_logged = bins_logged*1.0/numpy.sum(bins_logged)/(edges_logged[1]-edges_logged[0])
(bins_normal,edges_normal) = numpy.histogram(validation_set.flatten()[numpy.nonzero(validation_set.flatten())],bins=400)
bins_normal = bins_normal*1.0/numpy.sum(bins_normal)/(edges_normal[1:]-edges_normal[:-1])
pylab.subplot(332)
pylab.title('Averaged validation set')
xs = edges_normal[:-1]+(edges_normal[1]-edges_normal[0])/2
pylab.bar(xs,bins_normal,width=(edges_normal[1]-edges_normal[0])/2,color='b')
pylab.plot(xs,numpy.exp(-numpy.power(numpy.log(xs)-numpy.mean(logged),2)/(2*numpy.var(logged)))/(xs*numpy.sqrt(2*numpy.pi*numpy.var(logged))),'g',lw=2)
pylab.plot(xs,numpy.exp(-xs/numpy.mean(validation_set))/numpy.mean(validation_set),'r',lw=2)
pylab.axis(xmin=0,xmax=4)
pylab.subplot(335)
xs = edges_logged[:-1]+(edges_logged[1]-edges_logged[0])/2
pylab.plot(xs,bins_logged,'o')
xs = numpy.arange(edges_logged[0],edges_logged[-1],(edges_logged[-1]-edges_logged[0])/200)
pylab.plot(xs,numpy.exp(-numpy.power(xs-numpy.mean(logged),2)/(2*numpy.var(logged)))/numpy.sqrt(2*numpy.pi*numpy.var(logged)),'g',lw=2)
pylab.subplot(338, xscale='log',yscale='log')
xs = edges[:-1]+(edges[1:]-edges[:-1])/2
pylab.plot(xs,bins,'o')
xs = numpy.exp(numpy.arange(-4,10,0.05))
pylab.plot(xs,numpy.exp(-numpy.power(numpy.log(xs)-numpy.mean(logged),2)/(2*numpy.var(logged)))/(xs*numpy.sqrt(2*numpy.pi*numpy.var(logged))),'g',lw=2)
pylab.plot(xs,numpy.exp(-xs/numpy.mean(validation_set))/numpy.mean(validation_set),'r',lw=2)
pylab.axis(xmin=10e-3,xmax=20,ymin=10e-4,ymax=10)
pylab.xlabel('Firing rate')
(bins,edges) = numpy.histogram(val_pred_act.flatten()[numpy.nonzero(val_pred_act.flatten())],bins=numpy.exp(numpy.arange(-4,10,0.4)))
bins = bins*1.0/numpy.sum(bins)/(edges[1:]-edges[:-1])
logged = numpy.log(val_pred_act.flatten()[numpy.nonzero(val_pred_act.flatten())])
(bins_logged,edges_logged) = numpy.histogram(logged,bins=20)
bins_logged = bins_logged*1.0/numpy.sum(bins_logged)/(edges_logged[1]-edges_logged[0])
(bins_normal,edges_normal) = numpy.histogram(val_pred_act.flatten()[numpy.nonzero(val_pred_act.flatten())],bins=400)
bins_normal = bins_normal*1.0/numpy.sum(bins_normal)/(edges_normal[1:]-edges_normal[:-1])
pylab.subplot(333)
pylab.title('Model')
xs = edges_normal[:-1]+(edges_normal[1]-edges_normal[0])/2
pylab.bar(xs,bins_normal,width=(edges_normal[1]-edges_normal[0])/2,color='b')
pylab.plot(xs,numpy.exp(-numpy.power(numpy.log(xs)-numpy.mean(logged),2)/(2*numpy.var(logged)))/(xs*numpy.sqrt(2*numpy.pi*numpy.var(logged))),'g',lw=2)
pylab.plot(xs,numpy.exp(-xs/numpy.mean(val_pred_act))/numpy.mean(val_pred_act),'r',lw=2)
pylab.axis(xmin=0,xmax=4)
pylab.subplot(336)
xs = edges_logged[:-1]+(edges_logged[1]-edges_logged[0])/2
pylab.plot(xs,bins_logged,'o')
xs = numpy.arange(edges_logged[0],edges_logged[-1],(edges_logged[-1]-edges_logged[0])/200)
pylab.plot(xs,numpy.exp(-numpy.power(xs-numpy.mean(logged),2)/(2*numpy.var(logged)))/numpy.sqrt(2*numpy.pi*numpy.var(logged)),'g',lw=2)
pylab.subplot(339, xscale='log',yscale='log')
xs = edges[:-1]+(edges[1:]-edges[:-1])/2
pylab.plot(xs,bins,'o')
xs = numpy.exp(numpy.arange(-4,10,0.05))
pylab.plot(xs,numpy.exp(-numpy.power(numpy.log(xs)-numpy.mean(logged),2)/(2*numpy.var(logged)))/(xs*numpy.sqrt(2*numpy.pi*numpy.var(logged))),'g',lw=2)
pylab.plot(xs,numpy.exp(-xs/numpy.mean(val_pred_act))/numpy.mean(val_pred_act),'r',lw=2)
pylab.axis(xmin=10e-3,xmax=20,ymin=10e-4,ymax=10)
pylab.xlabel('Firing rate')
release_fig('Firing_rate_distribution_comparison.png')
pred_act_singletrial_flattened = numpy.reshape(pred_act_singletrial,(num_trials*num_pres,num_neurons))
lifetime_triggered_sparsness = TrevesRollsSparsness(numpy.mat(raw_validation_set_flattened))
population_triggered_sparsness = TrevesRollsSparsness(numpy.mat(raw_validation_set_flattened).T)
lifetime_predicted_sparsness = TrevesRollsSparsness(numpy.mat(pred_act_singletrial_flattened))
population_predicted_sparsness = TrevesRollsSparsness(numpy.mat(pred_act_singletrial_flattened).T)
lifetime_predicted_averaged_sparsness = TrevesRollsSparsness(numpy.mat(pred_act_avg))
population_predicted_averaged_sparsness = TrevesRollsSparsness(numpy.mat(pred_act_avg).T)
lifetime_triggered_averaged_sparsness = TrevesRollsSparsness(numpy.mat(numpy.mean(raw_validation_set,axis=0)))
population_triggered_averaged_sparsness = TrevesRollsSparsness(numpy.mat(numpy.mean(raw_validation_set,axis=0)).T)
f = pylab.figure(dpi=100,facecolor='w',figsize=(3,3))
pylab.hist(population_triggered_sparsness,bins=numpy.arange(0,1.0,0.025))
release_fig('RawPopulationSparsness.png')
f = pylab.figure(dpi=100,facecolor='w',figsize=(18,13))
f.subplots_adjust(hspace=0.3)
ax = pylab.subplot(4,2,1)
pylab.hist(lifetime_triggered_sparsness,bins=numpy.arange(0,1.0,0.025))
pylab.title('Lifetime sparseness')
pylab.axis(xmin=0.0,xmax=1.0)
ax.xaxis.set_major_locator(NullLocator())
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.ylabel('# neurons')
ax = pylab.subplot(4,2,2)
pylab.hist(population_triggered_sparsness,bins=numpy.arange(0,1.0,0.025))
pylab.title('Population sparseness')
pylab.axis(xmin=0.0,xmax=1.0)
ax.xaxis.set_major_locator(NullLocator())
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.text(1.02,0.5,'triggered',horizontalalignment ='left',verticalalignment='center',rotation='vertical',transform=pylab.gca().transAxes,clip_on=False,fontsize=16)
pylab.ylabel('# images')
#ax2 = pylab.gcf().add_axes(ax.get_position(), sharex=ax, frameon=False)
#ax.set_ylabel('triggered activities')
ax = pylab.subplot(4,2,3)
pylab.hist(lifetime_predicted_sparsness,bins=numpy.arange(0,1.0,0.025))
pylab.axis(xmin=0.0,xmax=1.0)
ax.xaxis.set_major_locator(NullLocator())
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.ylabel('# neurons')
ax = pylab.subplot(4,2,4)
pylab.hist(population_predicted_sparsness,bins=numpy.arange(0,1.0,0.025))
pylab.axis(xmin=0.0,xmax=1.0)
ax.xaxis.set_major_locator(NullLocator())
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.text(1.02,0.5,'predicted',horizontalalignment ='left',verticalalignment='center',rotation='vertical',transform=pylab.gca().transAxes,clip_on=False,fontsize=16)
pylab.ylabel('# images')
ax = pylab.subplot(4,2,5)
pylab.hist(lifetime_triggered_averaged_sparsness,bins=numpy.arange(0,1.0,0.025))
pylab.axis(xmin=0.0,xmax=1.0)
ax.xaxis.set_major_locator(NullLocator())
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.ylabel('# neurons')
ax = pylab.subplot(4,2,6)
pylab.hist(population_triggered_averaged_sparsness,bins=numpy.arange(0,1.0,0.025))
pylab.axis(xmin=0.0,xmax=1.0)
ax.xaxis.set_major_locator(NullLocator())
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.text(1.02,0.5,'triggered\ntrial\naveraged\n',horizontalalignment ='left',verticalalignment='center',rotation='vertical',transform=pylab.gca().transAxes,clip_on=False,fontsize=16,multialignment='center')
pylab.ylabel('# images')
ax = pylab.subplot(4,2,7)
pylab.hist(lifetime_predicted_averaged_sparsness,bins=numpy.arange(0,1.0,0.025))
pylab.axis(xmin=0.0,xmax=1.0)
ax.xaxis.set_major_locator(MaxNLocator(6))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.xlabel('Treves Rolls sparseness')
pylab.ylabel('# neurons')
ax = pylab.subplot(4,2,8)
pylab.hist(population_predicted_averaged_sparsness,bins=numpy.arange(0,1.0,0.025))
pylab.axis(xmin=0.0,xmax=1.0)
ax.xaxis.set_major_locator(MaxNLocator(6))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.text(1.02,0.5,'predicted\ntrial\naveraged\n',horizontalalignment ='left',verticalalignment='center',rotation='vertical',transform=pylab.gca().transAxes,clip_on=False,fontsize=16,multialignment='center')
pylab.xlabel('Treves Rolls sparseness')
pylab.ylabel('# images')
release_fig('Sparsness.png')
print numpy.shape(pred_act_avg)
print numpy.shape(numpy.mean(raw_validation_set,axis=0))
pylab.figure()
pylab.subplot(131)
pylab.imshow(validation_set,interpolation='nearest')
pylab.colorbar()
pylab.subplot(132)
pylab.imshow(pred_act_avg,interpolation='nearest')
pylab.colorbar()
pylab.subplot(133)
pylab.imshow(val_pred_act_nl,interpolation='nearest')
pylab.colorbar()
# analyze sparsification
from numpy.random import shuffle
print (num_trials,num_pres,num_neurons)
shuffled_raw_validation_set = numpy.zeros(numpy.shape(raw_validation_set))
for x in xrange(0,num_pres):
for y in xrange(0,num_neurons):
shuff_indexes = numpy.arange(0,num_trials,1)
shuffle(shuff_indexes)
shuffled_raw_validation_set[:,x,y] = numpy.array(raw_validation_set)[shuff_indexes,x,y]
shuffled_raw_validation_set_flattened = numpy.reshape(shuffled_raw_validation_set,(num_trials*num_pres,num_neurons))
shuffled_population_triggered_sparsness = TrevesRollsSparsness(numpy.mat(shuffled_raw_validation_set_flattened).T)
sums = numpy.sum(raw_validation_set,axis=2)
sums_shuffled = numpy.sum(shuffled_raw_validation_set,axis=2)
pylab.figure()
pylab.subplot(1,2,1)
pylab.title('Orignial data')
pylab.hist(sums.flatten(),bins=numpy.arange(0,200,10))
pylab.subplot(1,2,2)
pylab.hist(sums_shuffled.flatten(),bins=numpy.arange(0,200,10))
pylab.figure(dpi=100,facecolor='w',figsize=(6,5))
pylab.hist(numpy.array([population_triggered_sparsness.flatten(),shuffled_population_triggered_sparsness.flatten()]).T,bins=10,histtype='bar',label=['original','trial shuffled'])
pylab.xlabel('Treves Rolls sparseness of measured activities')
pylab.axis(xmin=0.0,xmax=1.0)
pylab.legend(loc='upper left')
release_fig('ShuffledSparsness.png')
# DISPERSAL ANALYSIS
scree_data = numpy.sort(numpy.var(raw_validation_set_flattened,axis=0).flatten())[::-1]
scree_data = scree_data/scree_data[0]
scree_model = numpy.sort(numpy.var(pred_act_singletrial_flattened,axis=0).flatten())[::-1]
scree_model = scree_model/scree_model[0]
raw_validation_set_normalized_flattened = raw_validation_set_flattened / numpy.sqrt(numpy.tile(numpy.var(raw_validation_set_flattened,axis=0),(numpy.shape(raw_validation_set_flattened)[0],1)))
scree_normalized = numpy.sort(numpy.var(raw_validation_set_normalized_flattened ,axis=0).flatten())[::-1]
scree_normalized = scree_normalized /scree_normalized[0]
population_normalized_triggered_sparsness = TrevesRollsSparsness(numpy.mat(raw_validation_set_normalized_flattened).T)
pylab.figure(dpi=100,facecolor='w',figsize=(15,6))
pylab.subplot(1,2,1)
pylab.plot(scree_data,'bo-',label='measured data')
pylab.plot(scree_model,'ro-',label='fitted model data')
pylab.ylabel('Variance of response distribution')
pylab.xlabel('Neuron identity')
pylab.legend()
pylab.subplot(1,2,2)
pylab.hist(numpy.array([population_triggered_sparsness.flatten(),population_normalized_triggered_sparsness.flatten()]).T,bins=10,histtype='bar',label=['origninal','equal variance'])
pylab.axis(xmin=0.0,xmax=1.0)
pylab.xlabel('Treves Rolls sparseness')
pylab.legend(loc='upper left')
release_fig('Dispersal.png')
node.add_data("validation_prediction_power",validation_prediction_power,force=True)
node.add_data("lifetime_triggered_sparsness",lifetime_triggered_sparsness,force=True)
node.add_data("population_triggered_sparsness",population_triggered_sparsness ,force=True)
node.add_data("lifetime_predicted_sparsness",lifetime_predicted_sparsness,force=True)
node.add_data("population_predicted_sparsness",population_predicted_sparsness,force=True)
node.add_data("lifetime_predicted_averaged_sparsness",lifetime_predicted_averaged_sparsness,force=True)
node.add_data("population_predicted_averaged_sparsness",population_predicted_averaged_sparsness,force=True)
node.add_data("lifetime_triggered_averaged_sparsness",lifetime_triggered_averaged_sparsness,force=True)
node.add_data("population_triggered_averaged_sparsness",population_triggered_averaged_sparsness,force=True)
node.add_data("shuffled_population_triggered_sparsness",shuffled_population_triggered_sparsness,force=True)
node.add_data("scree_data",scree_data,force=True)
node.add_data("scree_model",scree_model,force=True)
node.add_data("scree_normalized",scree_normalized,force=True)
#contrib.dd.saveResults(res,"newest_dataset.dat")
def TrevesRollsSparsness(mat):
# Computes Trevers Rolls Sparsness of data along columns
x,y = numpy.shape(mat)
trs = numpy.array(1-(numpy.power(numpy.mean(mat,axis=0),2))/(numpy.mean(numpy.power(mat,2),axis=0)+0.0000000000000000000001))[0]/(1.0 - 1.0/x)
return trs * (trs<=1.0)
def GenerateMultiplayerFigures():
contrib.modelfit.save_fig_directory='/home/antolikjan/Doc/reports/Sparsness/PieceWise/'
res = contrib.dd.loadResults("newest_dataset.dat")
animals = (
res.children[0].children[0].children[0].children[0],
res.children[4].children[0].children[0].children[0]
)
vpps = numpy.concatenate([a.data["validation_prediction_power"] for a in animals])
pylab.figure(dpi=100,facecolor='w',figsize=(6,5))
pylab.hist(vpps)
pylab.xlabel('Fraction of explained signal power ')
pylab.ylabel('# neurons')
release_fig('PredictionPower.png')